Abstract Book

S218

ESTRO 37

acquired at a single institution. Evaluation was performed on 22 OARs in the head and neck according to international consensus delineation guidelines, comprising the arytenoids, carotid arteries, buccal mucosas, brainstem, cerebellum, cerebrum, cricopharyngeal inlet, mandible, extended oral cavity, parotid and submandibular glands, thyroid, glottic and supraglottic area, pharynx constrictor muscles, cervical esophagus and spinal cord. The set of clinical cases was randomly divided into a training set (549), cross- validation set (40) and test set (109) for training of the DLC models. Training of DLC was performed on-site. DLC was compared against an atlas-based auto-segmentation (ABAS) method (WorkflowBox 1.4) that employed a representative set of 30 atlases selected from the training set, to contour the test images. A quantitative evaluation against ground-truth clinical contours was performed by computing the Dice similarity coefficient (Dice), and average distance (AD) between both sets of automatically generated contours and the manual clinical contours. Results Quantitative results for the test set are shown in Figure 1 and Table 1 for the considered OARs. Figure 1 (top) shows Dice values for ABAS (x-axis) and DLC (y-axis). DLC outperforms ABAS if the symbol lies above the bisector line. Similarly, Figure 1 (bottom) shows AD values in mm for ABAS (x-axis) and DLC (y-axis). In this case, DLC outperforms ABAS if the symbol lies below the bisector line. Results from the performed evaluation show DLC to significantly outperform ABAS for 17 out of 22 OARs considered.

Conclusion This quantitative investigation has shown that DLC significantly outperforms ABAS methods for the automatic contouring of the majority of OARs in head and neck cancer, particularly for structures with high anatomical variability, such as parotid and submandibular glands, thyroid or small and elongated structures, such as carotids, arytenoids, glottis and supraglottic area, pharynx constrictor muscles. This improvement can be explained considering the larger amount of flexibility allowed by the deep learning models compared to ABAS. Further evaluation will aim to quantify the impact on clinical workflow and clinical outcome of the observed accuracy improvements. OC-0419 Comparison of auto-contouring methods for regions of interest in prostate CT P. Aljabar 1 , D. Peressutti 1 , E. Brunenberg 2 , R. Smeenk 2 , R. Van Leeuwen 2 , M. Gooding 1 1 Mirada Medical Limited, Science Group, Ox ford, United Kingdom 2 Radboud University Medical Centre, Radiation Oncology, Nijmegen, The Netherlands Purpose or Objective Automated segmentation methods are an important part of clinical protocols for contouring regions of interest (ROIs) [1]. However, time and effort required to edit auto-contours before clinical use motivates improvements to automated methods. This study compares an established atlas-based automatic segmentation (ABAS) method against a recent deep learning contouring (DLC) approach. Both methods were used to contour ROIs in a group of prostate cancer patients.